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quality_gate

Read-only

Validates embodied-AI datasets against configurable publish rules and returns gate failures with validation evidence, covering fps, feature keys, scores, counts, NaN ratio, and anomalies.

Instructions

Evaluate configurable publish rules from an inline JSON object: expected fps/tolerance, required feature keys or kinds, minimum score/episodes/frames, maximum NaN ratio and anomaly-window counts. Returns gate failures plus validation evidence. Read-only and available as a background job.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path": "<path>"
    },
    "name": "quality_gate"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
rulesNoRule overrides; omitted keys use training-oriented defaults (min_score=80, max_errors=0).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover readOnly/destructive/openWorld, so the description correctly adds beyond them: it states what comes back ('gate failures plus validation evidence') and that the operation can run as a background job, which affects how an agent sequences the call with submit_job/job_status. It stops short of saying whether it blocks or how failures are structured.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core action and the rule inventory, with the return/safety note trailing. The rule list is dense but each clause maps to a real capability; no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the return-value burden and does summarize it ('gate failures plus validation evidence'). However, for a tool with a nested free-form `rules` object and an unspecified `path`, an agent still lacks the key detail of what `path` identifies and what the failure evidence looks like, so it is only adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 50% — `rules` is described in the schema while `path` is entirely undocumented. The description usefully expands the rule vocabulary (fps/tolerance, feature keys/kinds, min score/episodes/frames, NaN ratio, anomaly windows), which goes well past the schema's terse 'rule overrides' note, but it never says what `path` points to (dataset, project, config file), leaving half the parameters ambiguous.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource ('Evaluate configurable publish rules') and enumerates the rule categories it checks, so an agent knows exactly what the tool does. It doesn't name the nearest siblings (validate_dataset, quality_report) to draw a boundary, which keeps it from a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'publish rules' and the rule list imply the pre-publication gate context, and 'available as a background job' hints at the long-running path. But there is no explicit when-to-use, no when-not-to-use, and no pointer to validate_dataset or quality_report as alternatives, leaving the agent to infer the routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.